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Path tracking control is a critical task for legged robots, such as hexapods, requiring them to navigate complex environments toward a series of predefined goals. This paper presents a reinforcement learning (RL)-based framework for efficient path-tracking, integrating Truncated Quantile Critics (TQC) for local target-reaching with a high-level path-planning algorithm. By breaking down the global path into sequential waypoints, the framework enables adaptive navigation and autonomous locomotion. Real-world experiments using CricketBOT, a hexapod robot, demonstrate the system’s effectiveness in tracking control and minimising errors. The results highlight the potential of RL strategies for real-time deployment in complex environments.

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Reinforcement Learning for Legged Robots: Truncated Quantile Critics with Path Following Tracking

  • 会议论文

  • 发表日期：

    2025年

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  • Hoan Quang Le,
  • Peter So,
  • Ahmed Abdelrahman,
  • Lingyun Chen,
  • Valentin Le Mesle,
  • Abdalla Swikir,
  • Sami Haddadin

摘要

Path tracking control is a critical task for legged robots, such as hexapods, requiring them to navigate complex environments toward a series of predefined goals. This paper presents a reinforcement learning (RL)-based framework for efficient path-tracking, integrating Truncated Quantile Critics (TQC) for local target-reaching with a high-level path-planning algorithm. By breaking down the global path into sequential waypoints, the framework enables adaptive navigation and autonomous locomotion. Real-world experiments using CricketBOT, a hexapod robot, demonstrate the system’s effectiveness in tracking control and minimising errors. The results highlight the potential of RL strategies for real-time deployment in complex environments.